Statistical analysis for exploring environmental Citizen Science practices at ILTER

Alessandro Oggioni; Caterina Bergami

The dataset

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## import dataset
dataset <- readxl::read_excel("ILTER and Public Engagement_October2020_secondPart.xlsx")
dataset$age <- as.numeric(format(Sys.Date(), "%Y")) - dataset$Q33
rmarkdown::paged_table(dataset, options = list(rows.print = 15))

If you want to download the file, please visit the GitHub repo and remember to cite this article (oggioni2022?) or the dataset (oggioni2022?) (CHANGE IT with the citation of the dataset!!) if you want to use them for other publications or analysis.

General Information

The link to the survey was sent to all the ILTER site managers through the ILTER secretariat contact list (850 email recipients). The questionnaire remained open from the end of February to mid-September 2020 with two reminders sent within this period. In total, we received 163 responses with a completeness higher than or equal to the 50% (respondents completed at least the 50% of the survey). Based on an estimated 850-1000 participating scientists, our response rate is 16-19%.

The number of persons who accessed the survey (not necessarily finished it):

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totalAnswers <- dataset %>% 
  dplyr::filter(Finished == 'True' | Finished == 'False') %>% dplyr::count()

296

The response rate is:

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respRate <- round((totalAnswers/750)*100, 2)

39.47 %

Pool for the second part of the survey: number of answers with a completeness >= 75 % between column Q10-Q30:

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CSPool <- dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  # dplyr::filter(Finished == 'True') %>% # = 75
  dplyr::count()

The number of persons who finished the survey (no information about the completeness):

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completeAnswers <- dataset %>% 
  dplyr::filter(Finished == 'True') %>% dplyr::count()

142

The number of answers with a completeness >= 50 %:

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halfOfAnswers <- dataset %>% dplyr::filter(as.numeric(Progress) >= 50) %>% dplyr::count()

163

The number of answer where the reference to the ILTER site, via DEIMS.ID, was NOT indicated:

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noDEIMSAnswers <- dataset %>% 
  dplyr::filter(Q30 == 'NA') %>% dplyr::count()

201

The number of answer where the reference to the ILTER site, via DEIMS.ID, was indicated:

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DEIMSAnswers <- dataset %>% 
  dplyr::filter(Q30 != 'NA') %>% dplyr::count()

The number of answers with DEIMS.iD among them:

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CSPoolWithDEIMSID <- dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q30 != 'NA') %>% 
  dplyr::count()

The number of answers with LTER network information among them:

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CSPoolWithLTERNetwork <- dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(!is.na('LTERNetwork')) %>% 
  dplyr::count()

The number of participants in CS initiative among respondents:

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participationVSresponcesAll <- dataset %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::count()

The number of participants in CS initiative among respondents with a completeness >= 50 %:

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participationVSresponces <- dataset %>% 
  dplyr::filter(as.numeric(Progress) >= 50) %>%
  dplyr::filter(Q10 > 0) %>% 
  dplyr::count()

The number of CS initiatives declared among respondents with a completeness >= 50 %:

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csIntiatives <- dataset %>%
  dplyr::filter(as.numeric(Progress) >= 50) %>%
  dplyr::filter(Q10 > 0) %>%
  dplyr::select(Q10) %>%
  dplyr::summarise(sum(Q10))

392

Geographic distribution of responses

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## Join with ILTER DEIMS GeoInfo
# Connect and download layers from LTER-Europe's GeoSever
fileName <- tempfile()
download.file("https://data.lter-europe.net/geoserver/deims/wfs?SERVICE=WFS&VERSION=1.0.0&REQUEST=GetFeature&TYPENAME=deims:ilter_all_formal&SRSNAME=EPSG:4326", fileName)
request <- rwfs::GMLFile$new(fileName)
client <- rwfs::WFSCachingClient$new(request)
ilter_all_formal <- client$getLayer("ilter_all_formal")

## Reading layer `ilter_all_formal' from data source 
##   `/private/var/folders/p1/110rx8q101z0wn0bwh4njrcw0000gn/T/RtmpsiBiAb/filec2edd801479' 
##   using driver `GML'
## Simple feature collection with 754 features and 6 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -156.5648 ymin: -78 xmax: 175.085 ymax: 79
## CRS:           NA

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sitesOnSurvey <- ilter_all_formal[ilter_all_formal$deimsid %in% dataset$Q30, ]

htmltools::div(
    style = htmltools::css(width="100%", height='100%'),
    leaflet::leaflet(sitesOnSurvey) %>%
    leaflet::addTiles() %>%
    # addMouseCoordinates() %>%
    leaflet::setView(lng = 113.63962, lat = 23.16001, zoom = 1) %>%
    leaflet::addMarkers(
      clusterOptions = leaflet::markerClusterOptions(),
      popup = paste0(
              # "Name: <b>", sitesOnSurvey$name, "</b><br/>",
              "DEIMS.ID: <b><a target = 'blank' href = '", sitesOnSurvey$deimsid, "'>", sitesOnSurvey$deimsid, "</a></b><br/>"
            ),
       group = "Sites"
    ) %>%
    leaflet::addWMSTiles(
      'http://getit.lteritalia.it/geoserver/wms',
      layers = 'geonode:Zonobiome_poly',
      options = leaflet::WMSTileOptions(
        # styles = ,
        format = "image/png",
        transparent = T),
      group = "Biome"
    ) %>%
    leaflet.extras::addWMSLegend(
      position = 'topright',
      uri = 'http://getit.lteritalia.it/geoserver/wms?REQUEST=GetLegendGraphic&VERSION=1.0.0&FORMAT=image/png&WIDTH=20&HEIGHT=20&LAYER=geonode:Zonobiome_poly'
    ) %>%
    leaflet::addLayersControl(position = 'bottomright',
                              overlayGroups = c("Sites", "Biome"),
                              options = leaflet::layersControlOptions(collapsed = FALSE)
    )
)

Research questions

5a - How many citizen science initiatives at your LTER Site or LTSER Platform have you been involved in? This number should include current and past initiatives.

The percentage of participants in CS initiative among respondents with a completeness >= 50 % and the participants with a completeness >= 50 % of answers is:

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partiRate <- round((participationVSresponces/halfOfAnswers)*100, 1)

55.2 %

The average of CS projects among the participants with a completeness >= 75 % in the second part of survey is:

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average <- dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>%
  dplyr::select(Q10) %>%
  dplyr::summarise(average = round(mean(Q10, na.rm=TRUE), 1))

4.6

5b - Does participation in CS differ by gender of ILTER scientists?

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participationCSDifference <- dataset %>%
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>%
  dplyr::select(c(Q10, Q31:Q36)) #%>% View()
participationCSDifference$age <- as.numeric(format(Sys.Date(), "%Y")) - participationCSDifference$Q33
# Q35 Gender
participationCSDifference %>%
  dplyr::group_by(Q35) %>% 
  # dplyr::summarise(totalCSInitiative = sum(Q10)) %>% 
  dplyr::count(Q35) %>% 
  dplyr::filter(n > 1) %>%
  ggplot2::ggplot(ggplot2::aes(x = Q35, y = n)) +
  ggplot2::geom_bar(stat = "identity", fill = "orange") +
  ggplot2::xlab("Gender") + ggplot2::ylab("Participants in CS initiatives") +
  ggplot2::geom_text(ggplot2::aes(label = n), vjust = 1.6, color = "white", size = 3.5)+
  ggplot2::theme_classic()

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partiGender <- participationCSDifference %>%
  dplyr::select(Q10, Q35)
table1 <- gtsummary::tbl_summary(
  data = partiGender,
  label = list(
    Q10 = "CS projects declared by participant (Q10)",
    Q35 = "Gender participant(Q35)"
  )
)
table1

Characteristic	N = 76¹
CS projects declared by participant (Q10)	3.0 (2.0, 4.0)
Gender participant(Q35)
Female	29 (39%)
Male	45 (61%)
Unknown	2
¹ Median (IQR); n (%)

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partiGender %>%
  gtsummary::tbl_summary(
    by = Q35, # split table by group
    missing = "no", # don't list missing data separately
    statistic = list(all_continuous() ~ "{mean} ({sd})"),
    label = list(Q10 = "CS projects declared (Q10)")
  ) %>%
  gtsummary::add_n() %>% # add column with total number of non-missing observations
  gtsummary::add_p() %>% # test for a difference between groups
  gtsummary::modify_header(label = "**Variable**") %>% # update the column header
  gtsummary::bold_labels()

Variable	N	Female, N = 29¹	Male, N = 45¹	p-value²
CS projects declared (Q10)	74	5.5 (10.1)	4.1 (4.7)	0.8
¹ Mean (SD)
² Wilcoxon rank sum test

7a - Spatial scale of the CS initiatives

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(c(Q10, Q13)) %>% # 73
  dplyr::count(Q13) %>% 
  dplyr::filter(n > 1) %>% 
  ggplot2::ggplot(ggplot2::aes(x = Q13, y = n)) +
  ggplot2::geom_bar(stat = "identity", fill = "green4") +
  ggplot2::xlab("") + ggplot2::ylab("Number of projects") +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::geom_text(ggplot2::aes(label = n), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::theme_classic()

7a - Temporal scale of the CS initiatives

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(c(Q12, Q56)) %>% # 73
  dplyr::group_by(Q12, Q56) %>%
  dplyr::summarise(freq = n()) %>% 
  dplyr::filter(!is.na(Q12)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = Q12, y = Q56)) +
  ggplot2::geom_point(aes(size = freq), colour = "green4") +
  ggplot2::xlab("Is the project still active?") + 
  ggplot2::ylab("Number of projects") +
  ggplot2::labs(size = "n of years") +
  ggplot2::scale_size_continuous(
    breaks = c(2, 4, 6, 8),
    labels = c('<=2', '4', '6', '=>8')
  ) +
  ggplot2::theme_bw()

7b - Research focus

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(c(Q10, Q14)) %>% # 73
  dplyr::count(Q14) %>% 
  ggplot2::ggplot(ggplot2::aes(x = Q14, y = n)) +
  ggplot2::geom_bar(stat = "identity", fill = "green4") +
  ggplot2::xlab("Research focus") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = n), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::theme_classic()

7c - Participants number/year

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>%  
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q17, Q18:Q18_8_TEXT, Q19:Q19_4_TEXT) %>% # 73
  dplyr::group_by(Q17) %>%
  dplyr::summarise(numProj = n()) %>%
  dplyr::mutate(Q17 = forcats::fct_relevel(Q17, "Fewer than 25", "25-50", "51-100", "101-500", "More than 500", "NA")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = Q17, y = numProj)) +
  ggplot2::xlab("n participants/year") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_bar(stat = "identity", fill = "blue4") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::theme_classic()

7c - Type of participants

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>%  
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q17, Q18:Q18_8_TEXT, Q19:Q19_4_TEXT) %>% # 73
  dplyr::group_by(Q18) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::filter(numProj > 3) %>%
  ggplot2::ggplot(ggplot2::aes(x = Q18, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "blue4") +
  ggplot2::xlab("Type of participants") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::theme_classic()

7c - Undeserved communities

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q17, Q18:Q18_8_TEXT, Q19:Q19_4_TEXT) %>% # 73
  dplyr::group_by(Q19) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::mutate(Q19 = forcats::fct_relevel(
    Q19, 
    "Volunteers who have limited financial resources", "Volunteers who live in rural areas", "No undeserved communities", "Other"
  )) %>% 
  dplyr::filter(numProj > 6) %>% 
  ggplot2::ggplot(ggplot2::aes(x = Q19, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "blue4") +
  ggplot2::xlab("Undeserved communities") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::theme_classic()

7d - Partecipation frequency

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q20) %>% # 73
  dplyr::group_by(Q20) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::filter(!is.na(Q20)) %>% 
  dplyr::mutate(Q20 = forcats::fct_relevel(Q20, 
                                    "Once", "Two to three times", "Four to six times", "More than six times")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = Q20, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "blue4") +
  ggplot2::xlab("Participation frequency") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::theme_classic()

7e - Type of involvement of the participants in the CS initiatives

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matrix7e <- # dataset %>% 
  # dplyr::filter(as.numeric(Progress) >= 50) %>% 
  dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q21_1:Q21_13) %>%
  tidyr::gather(questions, typeOfInvol) %>% dplyr::group_by(questions, typeOfInvol) %>% dplyr::count() %>% dplyr::ungroup() %>% tidyr::spread(questions, n) %>% 
  t() %>% 
  data.frame(row.names(.), ., row.names = NULL) %>% 
  `colnames<-`(c('Activity in CS', 'High involvement', 'Moderate involvement', 'Not at all involved', 'Very high involvement', 'Very little involvement', 'NA')) %>% 
  .[-1,-1] %>% .[,-6] %>% 
  as.matrix() %>% 
  `rownames<-`(c(
    'Help define research questions',
    'Help interpret data and draw conclusions',
    'Help disseminate conclusions',
    'Help translate the results into action',
    'Help discuss results and ask new questions',
    'Help gather information and resources for research',
    'Help develop hypotheses',
    'Help design data collection methodologies',
    'Help collect samples or record data',
    'Help classify data',
    'Help process samples',
    'Help validate data', 
    'Help analyze data'
  )) %>% 
  reshape2::melt() 

matrix7e$Var2 <- factor(matrix7e$Var2, levels = c("Not at all involved", "Very little involvement", "Moderate involvement", "High involvement", "Very high involvement"))
matrix7e <- matrix7e[matrix7e$value!=0,]
matrix7e <- matrix7e[!is.na(matrix7e$value),]

ggplot2::ggplot(matrix7e, aes(x = Var2, y = Var1)) + 
  ggplot2::geom_raster(aes(fill = as.numeric(value))) + 
  ggplot2::scale_fill_gradient(low = "grey90", high = "red4", na.value = "grey10", guide = "colourbar") +
  ggplot2::labs(x = "Degree of Involvement", y = "Type of Involvement") +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::scale_y_discrete(labels = function(x) stringr::str_wrap(x, width = 30)) +
  ggplot2::labs(fill = "n of answers") +
  ggplot2::theme_classic() + ggplot2::theme(axis.text.x = element_text(size = 8, angle = 0, vjust = 0.3),
                          axis.text.y = element_text(size = 8),
                          plot.title = element_text(size = 11))

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ggplot2::ggplot(matrix7e, aes(x = Var2, y = Var1)) + 
  # ggplot2::geom_raster() +
  # ggplot2::scale_fill_gradient(low = "grey90", high = "red") +
  geom_point(aes(size = as.numeric(value))) + #, colour = value)) +
  # scale_color_brewer(palette = "RdYlBu") +
  ggplot2::labs(x = "Degree of Involvement", y = "Type of Involvement", title = "Matrix") +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::scale_y_discrete(labels = function(x) stringr::str_wrap(x, width = 30)) +
  ggplot2::scale_size_continuous(
    breaks = c(5, 10, 15, 20, 25),
    labels = c('<=5', '10', '15', '20', '>25')
  ) +
  ggplot2::theme_classic() + ggplot2::theme(axis.text.x = element_text(size = 9, angle = 0, vjust = 0.3),
                          axis.text.y = element_text(size = 9),
                          plot.title = element_text(size = 11))

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ggplot2::ggplot(matrix7e, aes(x = Var2, y = Var1, size = as.numeric(value), label = value)) +
  ggplot2::labs(x = "Degree of Involvement", y = "Type of Involvement", title = "Matrix") +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::scale_y_discrete(labels = function(x) stringr::str_wrap(x, width = 30)) +
  geom_text(size = as.numeric(matrix7e$value), aes(colour = as.numeric(matrix7e$value))) +
  ggplot2::labs(colour = "n of answers") +
  ggplot2::scale_colour_distiller(palette = "RdYlBu") +
  ggplot2::theme_classic()

7h - Training methodologies of the volunteers

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q22) %>% 
  dplyr::group_by(Q22) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::filter(!is.na(Q22)) %>% 
  dplyr::filter(numProj > 4) %>% 
  ggplot2::ggplot(ggplot2::aes(x = Q22, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "yellow4") +
  ggplot2::xlab("") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::theme_classic() + ggplot2::theme(axis.text.x = element_text(size = 4, angle = 0, vjust = 0.2),
                                   axis.text.y = element_text(size = 8))

7i - Data type

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>%  
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q23, Q23_6_TEXT) %>% 
  dplyr::group_by(Q23) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::filter(!is.na(Q23)) %>% 
  dplyr::filter(numProj > 3) %>%
  ggplot2::ggplot(ggplot2::aes(x = Q23, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "blueviolet") +
  ggplot2::xlab("") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 1)) +
  ggplot2::theme_classic() + ggplot2::theme(axis.text.x = element_text(size = 5, angle = 0, vjust = 0.3),
                                   axis.text.y = element_text(size = 8))

7i - Quality check

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>%  
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q25:Q25_9_TEXT) %>% 
  dplyr::group_by(Q25) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::filter(!is.na(Q25)) %>% 
  dplyr::filter(numProj > 3) %>%
  ggplot2::ggplot(ggplot2::aes(x = Q25, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "blueviolet") +
  ggplot2::xlab("") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::theme_classic()

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>%  
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q24, Q26:Q26_6_TEXT) %>%
  dplyr::group_by(Q24) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::filter(!is.na(Q24)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = Q24, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "chocolate4") +
  ggplot2::xlab("") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::theme_classic()

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q24, Q26:Q26_6_TEXT) %>% 
  dplyr::group_by(Q26) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::filter(!is.na(Q26)) %>% 
  dplyr::filter(numProj > 3) %>%
  ggplot2::ggplot(ggplot2::aes(x = Q26, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "chocolate4") +
  ggplot2::xlab("") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::theme_classic() + ggplot2::theme(axis.text.x = element_text(size = 8, angle = 0, vjust = 0.3),
                                   axis.text.y = element_text(size = 8))

7k - Ways to acknowledge participants

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dataset %>% 
  dplyr::filter(as.numeric(ProgressCS) >= 75) %>% 
  dplyr::filter(Q10 > 0) %>% 
  dplyr::select(Q10, Q27) %>%
  dplyr::group_by(Q27) %>%
  dplyr::summarise(numProj = n()) %>% 
  dplyr::filter(!is.na(Q27)) %>%
  dplyr::filter(numProj > 3) %>%
  ggplot2::ggplot(ggplot2::aes(x = Q27, y = numProj)) +
  ggplot2::geom_bar(stat = "identity", fill = "chocolate4") +
  ggplot2::xlab("") + ggplot2::ylab("Number of projects") +
  ggplot2::geom_text(ggplot2::aes(label = numProj), vjust = 1.6, color = "white", size = 3.5) +
  ggplot2::scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  ggplot2::theme_classic()

7l - Geographical distribution of the ILTER initiatives

Show code

# listOfAllSites <- ReLTER::get_ilter_generalinfo()
# saveRDS(listOfAllSites, file = "ilter_sitesData.rds")
listOfAllSites <- readRDS(file = "ilter_sitesData.rds")
# remove the sites without geometry
listOfAllSites <- listOfAllSites[c(1:1226, 1228:1237, 1239:1240, 1242:1243, 1248:1249), ]

siteWithDeimsId <- dataset %>% 
  dplyr::select(Q30) %>% 
  .[-160,] %>% 
  dplyr::filter(Q30 != "NA") %>% 
  dplyr::add_row(Q30 = c(
    "https://deims.org/664177a4-a21a-4f59-9601-00909e275868",
    "https://deims.org/5a38fc08-5257-4b13-8465-1d50ea166b95",
    "https://deims.org/96ba6c55-a555-4e96-a3e6-14d6dfe8785b",
    "https://deims.org/923cb154-83c9-444d-817a-cde7879c09b5"
  )) %>% 
  unique() # 84 DEIMS.iD
sitesOnSurvey <- listOfAllSites[listOfAllSites$uri %in% siteWithDeimsId$Q30, ] # 84 sites compared with ILTER formal sites

# collect biogeographicalRegion and biome from DEIMS site
sitesOnSurveyEnvChar <- lapply(
  as.list(sitesOnSurvey$uri),
  FUN = function(x) {ReLTER::get_site_info(x, category = c("EnvCharacts"))}
) %>% 
  dplyr::bind_rows() %>% 
  dplyr::select(uri, envCharacteristics.biogeographicalRegion, envCharacteristics.biome)
  
sitesOnSurvey_2 <- merge(x = sitesOnSurvey, y = sitesOnSurveyEnvChar, by.x = "uri", by.y = "uri", all = T)

biomeNum <- sitesOnSurvey_2$envCharacteristics.biome[-68] %>% unique() %>% length()
getPalette <- grDevices::colorRampPalette(RColorBrewer::brewer.pal(12, "Set3"))

# Biome map plot
library("rnaturalearth")
library("rnaturalearthdata")

world <- ne_countries(scale = "medium", returnclass = "sf")
ggplot2::ggplot(data = world) +
  ggplot2::geom_sf() +
  ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude") +
  ggplot2::scale_y_continuous(limits = c(-90, 90), expand = c(0, 0)) +
  ggplot2::scale_x_continuous(expand = c(0, 0)) +
  ggplot2::geom_sf(
    data = sitesOnSurvey_2$geometry[-68], 
    size = 1, 
    ggplot2::aes(
      color = sitesOnSurvey_2$envCharacteristics.biome[-68]
    ),
  ) + # feature 68 missing the information in DEIMS_SDR about the Biome
  ggplot2::scale_fill_manual(getPalette(biomeNum)) +
  ggplot2::ggtitle("iLTER Sites on survey") +
  ggplot2::scale_fill_discrete(name = "New Legend Title")

7l - Biogeographic distribution of the ILTER initiatives

Show code

# Biogeographical Region map plot
nc <- sf::st_read("../TeaBagCatalogue/Maps_export/Zonobiome_poly.shp", quiet = TRUE)
ggplot2::ggplot() +
  ggplot2::scale_y_continuous(limits = c(-90, 90), expand = c(0, 0)) +
  ggplot2::scale_x_continuous(expand = c(0, 0)) +
  ggplot2::geom_sf(data = nc, ggplot2::aes(fill = Legend), lwd = 0) +
  ggplot2::geom_sf(data = sitesOnSurvey_2$geometry[-68], color = "black", size = 1) + # feature 68 missing the information in DEIMS_SDR about the Biome
  ggplot2::ggtitle("iLTER Sites on survey") +
  ggplot2::scale_fill_discrete(name = "Biogeographical Region")

Acknowledgments

This article is created by Distill R package (Dervieux et al. 2022), the tables summary are made with gtsummary R package (Sjoberg et al. 2021).

Dervieux, Christophe, JJ Allaire, Rich Iannone, Alison Presmanes Hill, and Yihui Xie. 2022. Distill: R Markdown’ Format for Scientific and Technical Writing.

Sjoberg, Daniel D., Karissa Whiting, Michael Curry, Jessica A. Lavery, and Joseph Larmarange. 2021. “Reproducible Summary Tables with the Gtsummary Package.” The R Journal 13: 570–80. https://doi.org/10.32614/RJ-2021-053.

Statistical analysis for exploring environmental Citizen Science practices at ILTER

The dataset

General Information

Geographic distribution of responses

Research questions

5a - How many citizen science initiatives at your LTER Site or LTSER Platform have you been involved in? This number should include current and past initiatives.

5b - Does participation in CS differ by gender of ILTER scientists?

7a - Spatial scale of the CS initiatives

7a - Temporal scale of the CS initiatives

7b - Research focus

7c - Participants number/year

7c - Type of participants

7c - Undeserved communities

7d - Partecipation frequency

7e - Type of involvement of the participants in the CS initiatives

7h - Training methodologies of the volunteers

7i - Data type

7i - Quality check

7k - Ways to acknowledge participants

7l - Geographical distribution of the ILTER initiatives

7l - Biogeographic distribution of the ILTER initiatives

Acknowledgments

References

Citation

Statistical analysis for exploring environmental Citizen Science practices at ILTER

The dataset

General Information

Geographic distribution of responses

Research questions

5a - How many citizen science initiatives at your LTER Site or LTSER Platform have you been involved in? This number should include current and past initiatives.

5b - Does participation in CS differ by gender of ILTER scientists?

7a - Spatial scale of the CS initiatives

7a - Temporal scale of the CS initiatives

7b - Research focus

7c - Participants number/year

7c - Type of participants

7c - Undeserved communities

7d - Partecipation frequency

7e - Type of involvement of the participants in the CS initiatives

7h - Training methodologies of the volunteers

7i - Data type

7i - Quality check

7j - Ways to share data

7j - Ways to share findings

7k - Ways to acknowledge participants

7l - Geographical distribution of the ILTER initiatives

7l - Biogeographic distribution of the ILTER initiatives

Acknowledgments

References

Citation